lUse of Green Biosurfactants and Nanomaterials for Mining Residue and Effluent Remediation
Bibliographic record
Abstract
Removal of metals from mining effluents is challenging. Different novel approaches have used for remediation of the metal containing effluents. Although ultrafiltration can remove high molecular weight molecules, it is not effective for removal of low molecular weight pollutants. To increase the size of these pollutants, a rhamnolipid biosurfactant was utilized in micellar-enhanced ultrafiltration (MEUF) of heavy metals from contaminated waters. Various operating conditions were investigated and optimized for copper, zinc, nickel, lead and cadmium. Six contaminated wastewaters from metal refining industries were treated using two different membranes. The resulting heavy metal concentrations in the treated water were all significantly reduced to accord with the federal Canadian regulations. Another approach included an evaluation of the use of a biosurfactant, rhamnolipid, for the removal and reduction of hexavalent chromium from contaminated water. The initial chromium concentration, rhamnolipid concentration, pH and temperature affected the reduction efficiency. Complete reduction by rhamnolipid of initial Cr (VI) in water at optimum conditions (pH 6, 2% rhamnolipid concentration, 25oC) occurred at a low chromium concentration (10 ppm). Experiments were also conducted to investigate the effect of rhamnolipid on the remediation of chromium(VI) from water using iron nanoparticles. Iron nanoparticles were produced in the presence of different concentrations of rhamnolipid. Then, unmodified nanoparticles were treated with different concentrations of rhamnolipid and carboxymethyl cellulose. Furthermore the effect of the presence of rhamnolipid on reductive remediation of hexavalent chromium, Cr (VI), to trivalent, Cr (III), was investigated. At concentrations of 0.08 g/L iron and 2% (w/w) of rhamnolipid, the remediation of chromium increased by 123% in 15 hours compared with solutions containing only iron nanoparticles or only rhamnolipid. In addition, the applicability of iron/copper bimetallic nanoparticles for removal of arsenic from contaminated waters was investigated. Sorption tests in aqueous arsenic solutions at three different concentrations with various doses of nanoparticles were performed. Synthesized nanoparticles of hybrid Fe/Cu nanoparticles with a mean diameter of 13.17 nm were effective for removing arsenic from aqueous solutions. The Fe/Cu nanoparticle powder was found to be effective for removal of arsenic from water over a period of 18 months and has potential to be used for arsenic remediation from the aquatic environment in the long term. Overall, depending on the metal contamination, biosurfactants and/or nanoparticle addition may be effective for metal contamination effluent treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".